Spreading Dynamics of a Word-of-Mouth Model on Scale-Free Networks

Liu, Wenjin; Li, Tao; Liu, Xiongding; Xu, Hao

doi:10.1109/access.2018.2878522

Cited by 18 publications

(10 citation statements)

References 31 publications

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“…Unfortunately, such WOM propagation models only apply to homogeneous OSNs. A networkbased WOM propagation model classifies OSN individuals based on their states and their influences in OSNs [17], [18]. Unfortunately, such WOM propagation models are only applicable to some special OSNs such as scale-free networks.…”

Section: A Wom Propagation Modelmentioning

confidence: 99%

Dynamic Maintenance Strategy for Word-of-Mouth Marketing

et al. 2020

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After-sale service is an integrable part of marketing activities. The after-sale service experience of a consumer can be measured by the dynamic maintenance (DM) strategy used by the merchant, i.e., the instantaneous fractions of the maintenance cost paid by the merchant in the total maintenance cost at all time. This paper aims to develop an optimal DM strategy for a word-of-mouth (WOM) marketing campaign. First, we propose an individual-based WOM propagation model in which the effect of the DM strategy is accounted for. Second, we convert the original problem into an optimal control problem, where the objective functional stands for the expected marketing profit, each optimal control stands for an optimal DM strategy. Third, we derive the optimality system for the optimal control problem. By solving the optimality system, we get a potential optimal control. Next, through comparative experiments we conclude that the DM strategy associated with the potential optimal control outperforms most DM strategies in terms of the expected marketing profit. Therefore, we recommend this potential DM strategy. Finally, we examine the effect of some factors on the expected marketing profit for the potential DM strategy. Our findings help to enhance the marketing profit of a WOM marketing campaign.

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Section: A Wom Propagation Modelmentioning

confidence: 99%

Dynamic Maintenance Strategy for Word-of-Mouth Marketing

et al. 2020

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“…Among them, the research hotspots of the dissemination process mostly focus on the communication mode and mechanism [12], the public opinion monitoring and data mining of OWOM communication [13], and the re-distribution of brand OWOM [14], especially in the positive and negative OWOM communication issues. Regarding research methods, the research methods of OWOM communication show diversity when system dynamics [15], social network analysis [16], agent-based modeling and simulation [17,18], and viral marketing [4,19] are included. Among them, SIR and its improved model are in line with the characteristics of brand OWOM communication users, OWOM communication, and the wider range, which are also featured with excellent applicability in brand OWOM communication research [16,20].…”

Section: Enterprise Brand Owom Communicationmentioning

confidence: 99%

The Dynamic Evolution Mechanism of Heterogeneous OWOM—An Improved Viral Marketing Model

Liao

Zhang

2020

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The negative impact of brand negative online word-of-mouth (OWOM) on social images in social media is far greater than the promotion of positive OWOM. Thus, how to optimize brand image by improving the positive OWOM effect and slowing the negative OWOM communication has turned into an urgent problem for brand enterprises. On this basis, we analyze the evolution process of the OWOM communication group of the social media brand network based on the SOR (stimulus-organism-response) theory of psychology. Through constructing the heterogeneous brand OWOM communication dynamic model and conducting the multi-agent-based simulation experiment, the dynamic visualization of brand OWOM communication effect combined the thinking model of viral marketing is realized. Experiments show that the ability of brand communicators to persuade has a direct impact on the persistence and breadth of brand communication. When the acceptance of the consumer market is high, the negative OWOM of the brand has a relatively huge impact on consumers.

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“…Under the framework of heterogeneous complex network theory, the connection topology of nodes can be defined by node degree and the related degree distribution. Several complex network models have been studied for the epidemic model [8][9][10][11], alcoholism model [12], information spreading model [13][14][15][16][17], etc. But for wireless sensor network, there are few achievements: del Rey et al [18] established heterogeneous SIS and SIR models for malware propagation in WSNs; all nodes are separated into three compartments based on the classic SIR epidemic model.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Stabilization of Malware Propagation in Wireless Sensor Network via Aperiodically Intermittent White Noise

et al. 2020

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In this paper, we propose a novel heterogeneous model to describe the propagation dynamics of malware (viruses, worms, Trojan horses, etc.) in wireless sensor networks. Our model takes into consideration different battery-level sensor nodes contrary to existing models. In order to control the spread of malware, we design an aperiodically intermittent controller driven by white noise, which has striking advantages of lower cost and more flexible control strategy. We give a distinct condition on stability in probability one using graph-theoretical Lyapunov function and stochastic analysis method. Our results show that the nonlinear malware propagation system can be stabilized by intermittent stochastic perturbation under the intermittent time related to stochastic perturbation intensity. Our theoretical results can be applied to understand the observed mechanisms of malware and design interventions to control the spread of malware. Numerical simulations illustrate our analytical results clearly.

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Spreading Dynamics of a Word-of-Mouth Model on Scale-Free Networks

Cited by 18 publications

References 31 publications

Dynamic Maintenance Strategy for Word-of-Mouth Marketing

Dynamic Maintenance Strategy for Word-of-Mouth Marketing

The Dynamic Evolution Mechanism of Heterogeneous OWOM—An Improved Viral Marketing Model

Stochastic Stabilization of Malware Propagation in Wireless Sensor Network via Aperiodically Intermittent White Noise

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